When fixed feature dictionaries are used, this two-stage regression benefits from strong theoretical guarantees [1]}, [2]}. The use of pre-specified feature maps, however, limits the scope and flexibility of the method, especially if the treatment and proxies are high dimensional (e.g. images or text), and the causal relations are highly nonlinear. To overcome this, we propose to use adaptive features, expressed by neural nets, as described in the next section.
